Package config/template files with torchgen
This PR packages native_functions.yaml, tags.yaml and ATen/templates
with torchgen.
This PR:
- adds a step to setup.py to copy the relevant files over into torchgen
- adds a docstring for torchgen (so `import torchgen; help(torchgen)`
says something)
- adds a helper function in torchgen so you can get the torchgen root
directory (and figure out where the packaged files are)
- changes some scripts to explicitly pass the location of torchgen,
which will be helpful for the first item in the Future section.
Future
======
- torchgen, when invoked from the command line, should use sources
in torchgen/packaged instead of aten/src. I'm unable to do this because
people (aka PyTorch CI) invokes `python -m torchgen.gen` without
installing torchgen.
- the source of truth for all of these files should be in torchgen.
This is a bit annoying to execute on due to potential merge conflicts
and dealing with merge systems
- CI and testing. The way things are set up right now is really fragile,
we should have a CI job for torchgen.
Test Plan
=========
I ran the following locally:
```
python -m torchgen.gen -s torchgen/packaged
```
and verified that it outputted files.
Furthermore, I did a setup.py install and checked that the files are
actually being packaged with torchgen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78942
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74563
This is used inconsistently in all the generate_code program
invocations. Nevertheless, nothing consumes this flag, so we can
safely remove it.
This was removed in #25353.
ghstack-source-id: 152249818
Test Plan: Should be a no-op, rely on CI.
Reviewed By: malfet
Differential Revision: D35053096
fbshipit-source-id: 3ad19e83ca14649b514dc163c3caff6cbd118e14
(cherry picked from commit a43f05bb43553249caac3c3479986cbc45d286ae)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67496
gen_autograd.py doesn't use `Declarations.yaml` any more, and removing
the dependency allows it to run in parallel with
`tools/codegen/gen.py`.
Test Plan: Imported from OSS
Reviewed By: dagitses, ejguan
Differential Revision: D32027251
Pulled By: albanD
fbshipit-source-id: 2cc0bbe36478e6ec497f77a56ab8d01c76145703
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46244
- What does the generated binding code do?
The Python binding codegen produces code that takes the input list of
PyObjects, finds the matching ATen C++ function using PythonArgParser,
converts the PyObjects into C++ types and calls the ATen C++ function:
```
+--------+ parsing +------------------------+ binding +-----------------------+
| PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch |
+--------+ +------------------------+ +-----------------------+
```
- Are Python arguments 1-1 mapped to C++ arguments?
Python arguments might be reordered, packed, unpacked when binding to
C++ arguments, as illustrated below:
```
// Binding - Reorder & Packing
// aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None,
Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
Python Args Cpp Args
-----------------------------------------------------------
0: size size
1: names names
2: memory_format -------+
3: dtype -----+-|--> options
4: layout / |
5: device / +--> memory_format
6: pin_memory /
7: requires_grad -+
// Binding - Unpacking
// aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
Python Args Cpp Args
-----------------------------------------------------------
+----> max
/-----> max_values
0: input / self
1: dim / dim
2: keepdim / keepdim
3: out -----+
```
- Why do we want to rewrite the python binding codegen?
The old codegen takes Declarations.yaml as input. It doesn't distinguish
between Python arguments and C++ arguments - they are all mixed together
as a bag of non-typed dict objects. Different methods process these arg
objects and add new attributes for various different purposes. It's not so
obvious to figure out the semantics of these attributes. The complicated
binding logic happens implicitly and scatteredly.
```
+--------------------+
| Native Functions |
+--------------------+
|
|
v
+--------------------+
| Cpp Signatures |
+--------------------+
|
|
v
+--------------------+
| Declarations.yaml |
+--------------------+
| +-------------------------------------+
| +-------> | PythonArgParser Schema |
| | +-------------------------------------+
| | .
| | .
v | .
+--------------------+ +-------------------------------------+
| NonTyped Args Objs | --> | PythonArgParser -> Cpp Args Binding |
+--------------------+ +-------------------------------------+
| .
| .
| .
| +-------------------------------------+
+-------> | Cpp Function Dispatch |
+-------------------------------------+
```
This PR leverages the new immutable data models introduced in the new
aten codegen. It introduces dedicated data models for python schema.
This way, we can not only avoid subtle Declaration.yaml conversions but
also decouple the generation of python schema, python to c++ binding and
c++ function call.
The ultimate state will be like the following diagram:
```
+-------------------+ +-------------------------------------+
+-------> | Python Signatures | --> | PythonArgParser Schema |
| +-------------------+ +-------------------------------------+
| | .
| | .
| | .
+------------------+ | +-------------------------------------+
| Native Functions | +-------> | PythonArgParser -> Cpp Args Binding |
+------------------+ | +-------------------------------------+
| | .
| | .
| | .
| +-------------------+ +-------------------------------------+
+-------> | Cpp Signatures | --> | Cpp Function Dispatch |
+-------------------+ +-------------------------------------+
```
This PR has migrated the core binding logic from
tools/autograd/gen_python_functions.py to tools/codegen/api/python.py.
It produces the byte-for-byte same results (tested with #46243).
Will migrate the rest of gen_python_functions.py in subsequent PRs.
Test Plan: Imported from OSS
Reviewed By: bhosmer
Differential Revision: D24388874
Pulled By: ljk53
fbshipit-source-id: f88b6df4e917cf90d868a2bbae2d5ffb680d1841
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42629
How to approach reviewing this diff:
- The new codegen itself lives in `tools/codegen`. Start with `gen.py`, then read `model.py` and them the `api/` folder. The comments at the top of the files describe what is going on. The CLI interface of the new codegen is similar to the old one, but (1) it is no longer necessary to explicitly specify cwrap inputs (and now we will error if you do so) and (2) the default settings for source and install dir are much better; to the extent that if you run the codegen from the root source directory as just `python -m tools.codegen.gen`, something reasonable will happen.
- The old codegen is (nearly) entirely deleted; every Python file in `aten/src/ATen` was deleted except for `common_with_cwrap.py`, which now permanently finds its home in `tools/shared/cwrap_common.py` (previously cmake copied the file there), and `code_template.py`, which now lives in `tools/codegen/code_template.py`. We remove the copying logic for `common_with_cwrap.py`.
- All of the inputs to the old codegen are deleted.
- Build rules now have to be adjusted to not refer to files that no longer exist, and to abide by the (slightly modified) CLI.
- LegacyTHFunctions files have been generated and checked in. We expect these to be deleted as these final functions get ported to ATen. The deletion process is straightforward; just delete the functions of the ones you are porting. There are 39 more functions left to port.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bhosmer
Differential Revision: D23183978
Pulled By: ezyang
fbshipit-source-id: 6073ba432ad182c7284a97147b05f0574a02f763
Summary:
This PR move glu to Aten(CPU).
Test script:
```
import torch
import torch.nn.functional as F
import time
torch.manual_seed(0)
def _time():
if torch.cuda.is_available():
torch.cuda.synchronize()
return time.time()
device = "cpu"
#warm up
for n in [10, 100, 1000, 10000]:
input = torch.randn(128, n, requires_grad=True, device=device)
grad_output = torch.ones(128, n // 2, device=device)
for i in range(1000):
output = F.glu(input)
output.backward(grad_output)
for n in [10, 100, 1000, 10000]:
fwd_t = 0
bwd_t = 0
input = torch.randn(128, n, requires_grad=True, device=device)
grad_output = torch.ones(128, n // 2, device=device)
for i in range(10000):
t1 = _time()
output = F.glu(input)
t2 = _time()
output.backward(grad_output)
t3 = _time()
fwd_t = fwd_t + (t2 -t1)
bwd_t = bwd_t + (t3 - t2)
fwd_avg = fwd_t / 10000 * 1000
bwd_avg = bwd_t / 10000 * 1000
print("input size(128, %d) forward time is %.2f (ms); backwad avg time is %.2f (ms)."
% (n, fwd_avg, bwd_avg))
```
Test device: **skx-8180.**
Before:
```
input size(128, 10) forward time is 0.04 (ms); backwad avg time is 0.08 (ms).
input size(128, 100) forward time is 0.06 (ms); backwad avg time is 0.14 (ms).
input size(128, 1000) forward time is 0.11 (ms); backwad avg time is 0.31 (ms).
input size(128, 10000) forward time is 1.52 (ms); backwad avg time is 2.04 (ms).
```
After:
```
input size(128, 10) forward time is 0.02 (ms); backwad avg time is 0.05 (ms).
input size(128, 100) forward time is 0.04 (ms); backwad avg time is 0.09 (ms).
input size(128, 1000) forward time is 0.07 (ms); backwad avg time is 0.17 (ms).
input size(128, 10000) forward time is 0.13 (ms); backwad avg time is 1.03 (ms).
```
Fix https://github.com/pytorch/pytorch/issues/24707, https://github.com/pytorch/pytorch/issues/24708.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33179
Differential Revision: D19839835
Pulled By: VitalyFedyunin
fbshipit-source-id: e4d3438556a1068da2c4a7e573d6bbf8d2a6e2b9
Summary:
This PR aims to re-organize C++ API `torch::nn` folder structure in the following way:
- Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example:
`torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py`
`torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py`
`torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py`
`torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py`
- Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API)
- `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder.
- `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`.
- `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262
Differential Revision: D17422426
Pulled By: yf225
fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f
Summary:
Previously, we were not able to assign names to `nn::Sequential`'s submodules. This PR adds this feature to match the Python API. Example use:
```cpp
Sequential sequential(named_submodule({
{"linear", Linear(10, 3)},
{"conv2d", Conv2d(1, 2, 3)},
{"dropout", Dropout(0.5)},
{"batchnorm", BatchNorm(5)},
{"embedding", Embedding(4, 10)},
{"lstm", LSTM(4, 5)}
}));
```
It also enables loading parameters of Python `nn.Sequential` module with custom submodules names into C++ frontend, unblocking https://github.com/pytorch/vision/pull/728#issuecomment-466661344.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17552
Differential Revision: D14246834
Pulled By: yf225
fbshipit-source-id: 3030b5c5d68f6dd5d3e37ac4b4f98dc6d6d9ba72
